@inproceedings{a3725242f17c4dcf926ec876fb7461d5,
title = "Can LLMs Detect Display Issues? Uncovering the Impact of Prompting Techniques",
abstract = "While Large Language Models (LLMs) offer a promising, cost-effective alternative to conventional methods for detecting UI display issues, the effectiveness of various prompting techniques has not been systematically analyzed. This paper investigates the impact of various prompting techniques on the performance of LLMs in identifying UI display issues. Our analysis reveals that while some techniques significantly boost detection performance, others show minimal impact, and certain issue types, like Misalignment, remain challenging for all tested approaches.",
keywords = "Display Issue, Large Language Model, Prompt Engineering, UI",
author = "Ayoung Choi and Jongwook Jeong",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 38th Annual ACM Symposium on User Interface Software and Technology, UIST 2025 ; Conference date: 28-09-2025 Through 01-10-2025",
year = "2025",
month = sep,
day = "27",
doi = "10.1145/3746058.3758415",
language = "English",
series = "UIST Adjunct 2025 - Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology",
publisher = "Association for Computing Machinery, Inc",
editor = "Andrea Bianchi and Elena Glassman and Shengdong Zhao and Jeeeun Kim and Ian Oakley and Mackay, \{Wendy E.\}",
booktitle = "UIST Adjunct 2025 - Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology",
}